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Scientific data2024; 11(1); 488; doi: 10.1038/s41597-024-03338-5

De novo transcriptome assembly database for 100 tissues from each of seven species of domestic herbivore.

Abstract: Domesticated herbivores are an important agricultural resource that play a critical role in global food security, particularly as they can adapt to varied environments, including marginal lands. An understanding of the molecular basis of their biology would contribute to better management and sustainable production. Thus, we conducted transcriptome sequencing of 100 to 105 tissues from two females of each of seven species of herbivore (cattle, sheep, goats, sika deer, horses, donkeys, and rabbits) including two breeds of sheep. The quality of raw and trimmed reads was assessed in terms of base quality, GC content, duplication sequence rate, overrepresented k-mers, and quality score distribution with FastQC. The high-quality filtered RNA-seq raw reads were deposited in a public database which provides approximately 54 billion high-quality paired-end sequencing reads in total, with an average mapping rate of ~93.92%. Transcriptome databases represent valuable resources that can be used to study patterns of gene expression, and pathways that are related to key biological processes, including important economic traits in herbivores.
© 2024. The Author(s).
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Publication Date: 2024-05-11 PubMed ID: 38734729PubMed Central: PMC11088706DOI: 10.1038/s41597-024-03338-5Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This research developed a comprehensive transcriptome database by sequencing RNA from 100 different tissues in seven species of domesticated herbivores.
  • The data provide valuable insights into gene expression and biological pathways that support sustainable management and agricultural productivity of these animals.

Introduction and Purpose

  • Domesticated herbivores such as cattle, sheep, goats, deer, horses, donkeys, and rabbits are vital to global agriculture and food security.
  • These animals can thrive in diverse environments, including marginal lands, making them important for sustainable farming systems.
  • The study aims to understand the molecular biology of these species by creating an extensive transcriptome resource, which captures their gene expression across many tissue types.
  • Such molecular data can help improve breeding, health management, and productivity by revealing the genetic and biochemical pathways underlying key traits.

Methodology

  • Samples were collected from two female individuals per species, covering 100 to 105 different tissues per animal, yielding a highly diverse tissue representation.
  • Seven species were included: cattle, sheep (two breeds), goats, sika deer, horses, donkeys, and rabbits, encompassing major domestic herbivores.
  • RNA sequencing (RNA-seq) was performed to profile gene expression at a transcriptome-wide scale.
  • Quality control of sequencing reads involved:
    • Base quality checking
    • GC content distribution
    • Detection of duplicate sequences
    • Overrepresentation of specific short sequences (k-mers)
    • Assessment of overall quality scores
  • FastQC, a standard bioinformatics tool, was utilized for these quality assessments.

Data Generated

  • Approximately 54 billion paired-end RNA-seq reads were generated, representing a massive and rich dataset covering expression profiles from a wide range of tissues.
  • After filtering for quality, the reads demonstrated a high average mapping rate (~93.92%), indicating that most reads correctly aligned to reference genomes or transcriptomes.
  • The resulting dataset is publicly available, enabling future research and comparative studies in herbivore biology and genetics.

Significance and Applications

  • The transcriptome database captures gene expression variability across species, individuals, and tissues, providing a comprehensive molecular map of domestic herbivores.
  • It enables researchers to:
    • Investigate regulatory mechanisms controlling gene activity
    • Identify genes and pathways related to economically important traits such as growth, reproduction, and disease resistance
    • Understand species-specific adaptations to environment and management conditions
    • Support breeding programs through molecular marker discovery and functional genomics
  • Such data contribute to sustainable livestock production by informing strategies for genetic improvement and conservation.
  • The inclusion of multiple species and breeds increases the relevance and utility of the data for a wide agricultural community.

Cite This Article

APA
Wang Y, Huang Y, Zhen Y, Wang J, Wang L, Chen N, Wu F, Zhang L, Shen Y, Bi C, Li S, Pool K, Blache D, Maloney SK, Liu D, Yang Z, Li C, Yu X, Zhang Z, Chen Y, Xue C, Gu Y, Huang W, Yan L, Wei W, Wang Y, Zhang J, Zhang Y, Sun Y, Wang S, Zhao X, Luo C, Wang H, Ding L, Yang QY, Zhou P, Wang M. (2024). De novo transcriptome assembly database for 100 tissues from each of seven species of domestic herbivore. Sci Data, 11(1), 488. https://doi.org/10.1038/s41597-024-03338-5

Publication

Scientific data
ISSN: 2052-4463
NlmUniqueID: 101640192
Country: England
Language: English
Volume: 11
Issue: 1
Pages: 488
PII: 488

Researcher Affiliations

Wang, Yifan
  • State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China.
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
  • College of Life Science, Guizhou University, Guiyang, 550025, P. R. China.
Huang, Yiming
  • State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China.
  • Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
Zhen, Yongkang
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Wang, Jiasheng
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Wang, Limin
  • State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China.
Chen, Ning
  • State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China.
Wu, Feifan
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Zhang, Linna
  • Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
Shen, Yizhao
  • College of Animal Science and Technology, Hebei Agricultural University, Baoding, 071033, P. R. China.
Bi, Congliang
  • College of Life Science, Linyi University, Linyi, 276005, P. R. China.
Li, Song
  • College of Life Science, Guizhou University, Guiyang, 550025, P. R. China.
Pool, Kelsey
  • UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia.
Blache, Dominique
  • UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia.
Maloney, Shane K
  • UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia.
Liu, Dongxu
  • Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
Yang, Zhiquan
  • Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
Li, Chuang
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Yu, Xiang
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Zhang, Zhenbin
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Chen, Yifei
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Xue, Chun
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Gu, Yalan
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Huang, Weidong
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Yan, Lu
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Wei, Wenjun
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Wang, Yusu
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Zhang, Jinying
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Zhang, Yifan
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Sun, Yiquan
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China.
Wang, Shengbo
  • Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
Zhao, Xinle
  • Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
Luo, Chengfang
  • Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
Wang, Haodong
  • Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
Ding, Luoyang
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China. luoyang.ding@uwa.edu.au.
  • UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia. luoyang.ding@uwa.edu.au.
Yang, Qing-Yong
  • State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China. yqy@mail.hzau.edu.cn.
  • Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China. yqy@mail.hzau.edu.cn.
Zhou, Ping
  • State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P. R. China. zhpxqf@163.com.
Wang, Mengzhi
  • College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009, P. R. China. mzwang@yzu.edu.cn.

MeSH Terms

  • Animals
  • Cattle / genetics
  • Female
  • Rabbits / genetics
  • Databases, Genetic
  • Deer / genetics
  • Equidae / genetics
  • Goats / genetics
  • Herbivory
  • Horses / genetics
  • Sheep / genetics
  • Transcriptome

Conflict of Interest Statement

The authors declare no competing interests.

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Citations

This article has been cited 2 times.
  1. Jia X, Li J, Zhang Y, Tian B, Mao S, Liu J, Qian W. Transcriptomic profiling of rumen epithelium, liver, and muscle reveals tissue-specific gene expression patterns in Hu sheep.. BMC Genomics 2025 Nov 14;26(1):1115.
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